| Characteristic | N = 1471 |
|---|---|
| Age | – |
| Median (Q1, Q3) | 37 (34, 44) |
| Unknown | 13 |
| Age group | – |
| age ≥15-<25 | 1/134 (0.7%) |
| age ≥25-<35 | 45/134 (34%) |
| age ≥35-<45 | 62/134 (46%) |
| age ≥45-<55 | 19/134 (14%) |
| age ≥55 | 7/134 (5.2%) |
| Unknown | 13 |
| Sex | – |
| Female | 38/134 (28%) |
| Male | 96/134 (72%) |
| Unknown | 13 |
| Period | – |
| First Three Days | 74/147 (50%) |
| Last Two Days | 73/147 (50%) |
| Years of Professional Experience | – |
| Median (Q1, Q3) | 11 (8, 17) |
| Unknown | 13 |
| Experience in VHF Response | 101/134 (75%) |
| Unknown | 13 |
| The IDTM is an Advantage to the Systems Used in the Past | 117/121 (97%) |
| Unknown | 26 |
| Envisioning Usage of IDMT in the Future | – |
| Yes | 121/121 (100%) |
| Unknown | 26 |
| 1 n/N (%) | |
| Characteristic | Overall N = 1471 |
First Three Days N = 741 |
Last Two Days N = 731 |
p-value |
|---|---|---|---|---|
| Age | – | – | – | – |
| Median (Q1, Q3) | 37 (34, 44) | 37 (34, 43) | 37 (34, 44) | – |
| Unknown | 13 | 8 | 5 | – |
| Age group | – | – | – | – |
| age ≥15-<25 | 1/134 (0.7%) | 1/66 (1.5%) | 0/68 (0%) | – |
| age ≥25-<35 | 45/134 (34%) | 22/66 (33%) | 23/68 (34%) | – |
| age ≥35-<45 | 62/134 (46%) | 32/66 (48%) | 30/68 (44%) | – |
| age ≥45-<55 | 19/134 (14%) | 8/66 (12%) | 11/68 (16%) | – |
| age ≥55 | 7/134 (5.2%) | 3/66 (4.5%) | 4/68 (5.9%) | – |
| Unknown | 13 | 8 | 5 | – |
| Sex | – | – | – | – |
| Female | 38/134 (28%) | 18/66 (27%) | 20/68 (29%) | – |
| Male | 96/134 (72%) | 48/66 (73%) | 48/68 (71%) | – |
| Unknown | 13 | 8 | 5 | – |
| Years of Professional Experience | – | – | – | – |
| Median (Q1, Q3) | 11 (8, 17) | 11 (8, 17) | 12 (8, 18) | – |
| Unknown | 13 | 8 | 5 | – |
| Experience in VHF Response | 101/134 (75%) | 50/66 (76%) | 51/68 (75%) | – |
| Unknown | 13 | 8 | 5 | – |
| The IDTM is an Advantage to the Systems Used in the Past | 117/121 (97%) | 58/61 (95%) | 59/60 (98%) | – |
| Unknown | 26 | 13 | 13 | – |
| Envisioning Usage of IDMT in the Future | – | – | – | – |
| Yes | 121/121 (100%) | 61/61 (100%) | 60/60 (100%) | – |
| Unknown | 26 | 13 | 13 | – |
| 1 n/N (%) | ||||
Overall, 96.7% of participants considered IDTM an advantage to the systems used in the past and 100% are envisioning the usage of IDMT in the future (see Cohort Description above).
| Category | Average1 | Std Dev. | Std Err. | 95% CI | 2 |
|---|---|---|---|---|---|
| Attractiveness | 2.337 | 0.867 | 0.079 | 2.18-2.49 | ▲ |
| Perspicuity | 1.853 | 1.078 | 0.098 | 1.66-2.05 | ▲ |
| Novelty | 1.463 | 1.106 | 0.101 | 1.26-1.66 | ▲ |
| Stimulation | 2.324 | 0.916 | 0.083 | 2.16-2.49 | ▲ |
| Dependability | 1.897 | 0.952 | 0.087 | 1.73-2.07 | ▲ |
| Efficiency | 2.132 | 0.942 | 0.086 | 1.96-2.3 | ▲ |
| 1 Values between -0.8and 0.8 represent a more or less neutral evaluation of the corresponding scale, values > 0,8 represent a positive evaluation and values < -0,8 represent a negative evaluation. | |||||
| 2 ▲ Positive Evaluation | ■︎ Neutral | ▼ Negative Evaluation) | |||||
The benchmark classifies a product into 5 categories (per scale):
Excellent: In the range of the 10% best results.
Good: 10% of the results in the benchmark data set are better and 75% of the results are worse.
Above average: 25% of the results in the benchmark are better than the result for the evaluated product, 50% of the results are worse.
Below average: 50% of the results in the benchmark are better than the result for the evaluated product, 25% of the results are worse.
Bad: In the range of the 25% worst results.
Each item was rated on a 7-point scale from the left concept to the right. Lower values indicate stronger agreement with the first term (e.g., ‘Annoying’), while higher values reflect stronger agreement with the second (e.g., ‘Enjoyable’).
We used an ordinal logistic regression model to account for the ordered nature of the response scale. This allowed us to estimate the likelihood of each response level across time periods or experience in VHF response and visualize these shifts using the predicted probability plot.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Based on the number of identical answers (in the original answer scale in “Data”). If a participant for example crosses for all items the middle category (“4”) this can hardly be accepted as a serious response. Such answers are most likely result from an attempt to quickly finish the survey without much thinking. An analysis of several UEQ data sets suggests a heuristic to remove answers that show for more than 15 items the same response. This heuristic is implemented in the Column “Critical Length”. See the UEQ handbook for details.
Here, we have 4 participants who answered the same answer ≥ 16
To detect such suspicious answers, a simple heuristic is used. All items in a scale should measure a similar UX quality aspect. The idea behind the heuristic is to check how much the best and worst evaluation of an item in a scale differs. If there is a big difference (>3) this is seen as an indicator for a problematic data pattern. Of course such situations can also result from random response errors or a misunderstanding of an item. Thus, it makes no sense to delete a response if this occurs just for a single scale. But if this is true for 2 or 3 scales this is of course a clear hint that the response is somehow suspicious.
Here, we have 22 participants who had large difference between responses (>3) across 2 or 3 scales (highlighted in red below)
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.